{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the results -- Scenario I\n",
    "\n",
    "In this notebook, we plot the results , i.e., the spectral efficiency obtained by using the *hybrid*, the *E2E*, and the *delay blind* approach under Scenario I. We assume that there are trained models stored in a folder to load and directly test. \n",
    "\n",
    "Due to storage limitations the **trained models** are currently not available in this repository. They **can be found at** https://kth.box.com/s/tcd7y7rg3yau75kctw3regmyns8kfkr6, under the folder *Trained_models_ScenarioI*. The test dataset can also be found at the same link, in the folder *Datasets*.\n",
    "\n",
    "Instead of running the models, the reader can directly load the spectral efficiencies obtained by the three methods over the test datasets, by setting the parameter `load_from_memory` to `True`. \n",
    "\n",
    "The name of the trained neural network models are the following:\n",
    "\n",
    "- ANN_MCS_NO_PRED_DELAY_X_DP_Y_SNR_Z for the case of the *delay-blind* appraoch\n",
    "- ANN_E2E_MCS_PRED_DELAY_X_DP_Y_SNR_Z for the case of the *E2E* approach\n",
    "- ANN_MCS_PRED_WIENER_DELAY_X_DP_Y_SNR_Z for the case of the *hybrid* appraoch\n",
    "\n",
    "where X indicates the values of the delay, where Y indicates the relative user velocity, and where Z indicates the SNR.\n",
    "\n",
    "To give an example: ANN_MCS_PRED_WIENER_DELAY_1_DP_111_SNR_5\n",
    "\n",
    "The relative velocity of 60 km/h (i.e. 16.67 m/s) corresponds to a doppler of 111.11 Hz (i.e. 111 if cast to integer) in our setup, given that the carrier frequency we work with is 2 GHz. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries and utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import utilities as utils\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "matplotlib.rcdefaults()\n",
    "matplotlib.rcParams.update( { 'font.size': 22,\n",
    "                              'lines.linewidth': 4,\n",
    "                              'lines.markersize': 15 } )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%run setup.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that 'Datasets/ITU_VEHICULAR_B_5000_60kmph.npy' can be found at the link https://kth.box.com/s/tcd7y7rg3yau75kctw3regmyns8kfkr6\n",
    "# in the folder Datasets\n",
    "TEST_SET = 'Datasets/ITU_VEHICULAR_B_5000_60kmph.npy'\n",
    "relative_speed = 16.67 # m/s It must be consistent with the doppler of the loaded dataset\n",
    "DOPPLER = (2e9 / 3e8) * relative_speed\n",
    "DATASET = np.load( TEST_SET, allow_pickle = True )[()]\n",
    "NROF_FRAMES, NROF_SUBCARRIERS, NROF_SNRS = DATASET['channel'].shape\n",
    "NROF_MCS = len( DATASET[ 'block_sizes' ] )\n",
    "BLOCK_SIZES = DATASET[ 'block_sizes' ]\n",
    "# Maximum feedback delat that we consider\n",
    "maximum_delay = 9\n",
    "# Snrs set that we consider\n",
    "snrs_set = [5,15, 25]\n",
    "tx_bw = 15e3 * 72 # Tranmission Bandwidth\n",
    "load_from_memory = True # Flag to skip running the models and directly load the spectral efficiencies from the memory\n",
    "\n",
    "tput_HYBRID = []\n",
    "tput_DELAY_BLIND = []\n",
    "tput_E2E = []\n",
    "mean_squared_error_no_prediction = []\n",
    "mean_squared_error_with_prediction = []\n",
    "\n",
    "if load_from_memory == True:\n",
    "      \n",
    "    tput_DELAY_BLIND = np.load('Trained_models_ScenarioI_var/tput_DELAY-BLIND_sc_I_111.npy')\n",
    "    tput_HYBRID = np.load('Trained_models_ScenarioI_var/tput_HYBRID_sc_I_111.npy')\n",
    "    tput_E2E = np.load('Trained_models_ScenarioI_var/tput_E2E_sc_I_111.npy')\n",
    "    mean_squared_error_with_prediction = np.load('Trained_models_ScenarioI_var/MSE_sc_I_111.npy')\n",
    "    \n",
    "else:\n",
    "    for DELAY in range( 0, maximum_delay + 1):\n",
    "\n",
    "        for SNR in snrs_set:\n",
    "\n",
    "            print( 'Simulating a channel reporting delay of %d ms and SNR of %d'%( DELAY,SNR ) )   \n",
    "            E2E_MODEL_FILE = 'Trained_models_ScenarioI/ANN_E2E_MCS_PRED_DELAY_%d_DP_%d_SNR_%d.h5'%(DELAY,DOPPLER,SNR )\n",
    "            DELAY_BLIND_MODEL_FILE = 'Trained_models_ScenarioI/ANN_MCS_NO_PRED_DELAY_%d_DP_%d_SNR_%d.h5'%(DELAY,DOPPLER,SNR)\n",
    "            HYBRID_MODEL_FILE = 'Trained_models_ScenarioI/ANN_MCS_PRED_WIENER_DELAY_%d_DP_%d_SNR_%d.h5'%( DELAY,DOPPLER,SNR ) \n",
    "           \n",
    "                \n",
    "            tputs, \\\n",
    "        error_rates, \\\n",
    "        mse, \\\n",
    "        ack_prob    = computation( TEST_SET,\n",
    "                                   HYBRID_MODEL_FILE,\n",
    "                                   DELAY_BLIND_MODEL_FILE,\n",
    "                                   E2E_MODEL_FILE,\n",
    "                                   DELAY,\n",
    "                                   DOPPLER,\n",
    "                                   noise = True,\n",
    "                                   train_fraction = 0.2)\n",
    "\n",
    "\n",
    "            tput_DELAY_BLIND.append( tputs['ann_delayed'] )\n",
    "            tput_HYBRID.append( tputs['ann_ch_pr'] )\n",
    "            tput_E2E.append( tputs['ann_e2e'] )\n",
    "\n",
    "            mean_squared_error_with_prediction.append( mse['ann_ch_pr'] )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_tput_delayed = np.zeros([(maximum_delay + 1), len(snrs_set)]) \n",
    "results_tput_ch_pr = np.zeros([(maximum_delay + 1), len(snrs_set)]) \n",
    "results_e2e_tput = np.zeros([(maximum_delay + 1), len(snrs_set)]) \n",
    "delays = range( 0, maximum_delay + 1)\n",
    "\n",
    "for delay_index in range(0,maximum_delay + 1):\n",
    "    \n",
    "    for snr_index in [0,1,2]:\n",
    "        \n",
    "        results_tput_delayed[delay_index,snr_index] = (tput_DELAY_BLIND[len(snrs_set)*delay_index+snr_index][snrs_set[snr_index]-1] )/ tx_bw\n",
    "        results_tput_ch_pr[delay_index,snr_index] = (tput_HYBRID[len(snrs_set)*delay_index+snr_index][snrs_set[snr_index]-1])/ tx_bw\n",
    "        results_e2e_tput[delay_index,snr_index] = (tput_E2E[len(snrs_set)*delay_index+snr_index][snrs_set[snr_index]-1])/ tx_bw\n",
    "\n",
    "plt.figure( figsize = [ 25, 15 ] )\n",
    "plt.ylabel('Spectral efficiency [(bit/s)/Hz]',fontsize = 'xx-large')\n",
    "plt.xlabel('Delay [ms]',fontsize = 'xx-large')\n",
    "\n",
    "plt.semilogy( delays, results_tput_delayed[:,0], marker = 'o', color='b', linestyle = ':', label = 'Delay-blind, SNR=5dB' )\n",
    "plt.semilogy( delays, results_tput_ch_pr[:,0], marker = 'v', color='b', label = 'Hybrid, SN R= 5dB' )\n",
    "plt.semilogy( delays, results_e2e_tput[:,0], marker = '*', linestyle='--', color='b',label = 'E2R, SNR = 5dB' )\n",
    "\n",
    "plt.semilogy( delays, results_tput_delayed[:,1], marker = 'o', color ='r', linestyle =':',label = 'Delay-blind, SNR = 15dB' )\n",
    "plt.semilogy( delays, results_tput_ch_pr[:,1], marker = 'v', color ='r', label ='Hybrid, SNR = 15dB' )\n",
    "plt.semilogy( delays, results_e2e_tput[:,1], marker = '*', linestyle ='--', color ='r',label='E2E, SNR = 15dB' )\n",
    "\n",
    "plt.semilogy( delays, results_tput_delayed[:,2], marker = 'o', color ='g',linestyle = ':', label = 'Delay-blind, SNR = 25dB' )\n",
    "plt.semilogy( delays, results_tput_ch_pr[:,2], marker = 'v', color ='g', label = 'Hybrid, SNR = 25dB' )\n",
    "plt.semilogy( delays, results_e2e_tput[:,2], marker = '*', linestyle ='--',color = 'g', label = 'E2E, SNR = 25dB' )\n",
    "\n",
    "plt.xticks(np.arange(0, 10, step = 1))\n",
    "\n",
    "plt.grid(which = 'both')\n",
    "plt.legend()\n",
    "plt.legend(fontsize = 'x-large',loc = 'lower left', ncol = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tx_bw = 15e3 * 72 # Tranmission Bandwidth\n",
    "mse_no=np.zeros([maximum_delay + 1,len(snrs_set)])\n",
    "mse_with=np.zeros([maximum_delay + 1,len(snrs_set)])\n",
    "\n",
    "\n",
    "\n",
    "for delay_index in range(0,maximum_delay+ 1):\n",
    "    \n",
    "    for snr_index in [0,1,2]:\n",
    "\n",
    "        mse_with[delay_index,snr_index] = (mean_squared_error_with_prediction[len(snrs_set)*delay_index+snr_index][snrs_set[snr_index]-1])/ tx_bw\n",
    "\n",
    "plt.figure( figsize = [ 25, 15 ] )\n",
    "plt.ylabel('MSE',fontsize = 'xx-large')\n",
    "plt.xlabel('Delay [ms]',fontsize = 'xx-large')\n",
    "\n",
    "plt.semilogy( delays, mse_with[:,0], marker = 'v',color='black',label='With prediction , SNR=5dB' )\n",
    "plt.grid(which = 'both')\n",
    "plt.legend()\n",
    "plt.legend(fontsize = 'xx-large',loc = 'lower right', ncol = 3)\n",
    "plt.show()"
   ]
  }
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